{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# %load /Users/hwayment/ipynb_defaults.py\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%pylab inline\n",
    "import os, sys\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('ticks')\n",
    "sns.set_context('paper')\n",
    "\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sys.path.extend(['/mnt/c/Users/abreha.b/OneDrive - Stanford/rotations/das/arnie', '/mnt/c/Users/abreha.b/OneDrive - Stanford/rotations/das/'])\n",
    "#os.environ[\"ARNIEFILE\"] = '/mnt/c/Users/abreha.b/OneDrive - Stanford/rotations/das/arnie/run.arnie'\n",
    "#sys.path.append('/mnt/c/Users/abreha.b/OneDrive - Stanford/rotations/das/DegScore')\n",
    "\n",
    "sys.path.append('..')\n",
    "import assign_loop_type\n",
    "from assign_loop_type import write_loop_assignments\n",
    "\n",
    "\n",
    "from DegScore import DegScore\n",
    "from arnie.sample_structures import sample_structures\n",
    "\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code to reproduce DegScore from Kaggle datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ensemble(sequence, n=1000):\n",
    "    structs = sample_structures(sequence, n_samples=n, package='vienna')\n",
    "    structs = [list(write_loop_assignments(s)) for s in structs]\n",
    "    return np.array(structs)\n",
    "\n",
    "def encode_input(df, window_size=1, pad=10, seq=True, struct=True, ensemble_size=0):\n",
    "    '''Creat input/output for regression model for predicting structure probing data.\n",
    "    Inputs:\n",
    "    \n",
    "    dataframe (in EternaBench RDAT format)\n",
    "    window_size: size of window (in one direction). so window_size=1 is a total window size of 3\n",
    "    pad: number of nucleotides at start to not include\n",
    "    seq (bool): include sequence encoding\n",
    "    struct (bool): include bpRNA structure encoding\n",
    "    \n",
    "    Outputs:\n",
    "    Input array (n_samples x n_features): array of windowed input features\n",
    "    feature_names (list): feature names\n",
    "    \n",
    "    '''\n",
    "    #MAX_LEN = 68\n",
    "    BASES = ['A','U','G','C']\n",
    "    STRUCTS = ['H','E','I','M','B','S']\n",
    "    \n",
    "    inpts = []\n",
    "    labels = []\n",
    "\n",
    "    feature_kernel=[]\n",
    "    if seq:\n",
    "        feature_kernel.extend(BASES)\n",
    "    if struct:\n",
    "        feature_kernel.extend(STRUCTS)\n",
    "\n",
    "    feature_names = ['%s_%d' % (k, val) for val in range(-1*window_size, window_size+1) for k in feature_kernel]\n",
    "    \n",
    "    for i, row in tqdm(df.iterrows(), desc='Encoding inputs', total=len(df)):\n",
    "        MAX_LEN = len(row['sequence'])-39 #68 for RYOS-I\n",
    "        \n",
    "        arr = np.zeros([MAX_LEN,len(feature_kernel)])\n",
    "        \n",
    "        if ensemble_size > 0: # stochastically sample ensemble\n",
    "            ensemble = get_ensemble(row['sequence'], n=ensemble_size)\n",
    "        else: # use MEA structure\n",
    "            ensemble = np.array([list(row['predicted_loop_type'])])\n",
    "\n",
    "        for index in range(pad,MAX_LEN):\n",
    "            ctr=0\n",
    "\n",
    "            #encode sequence\n",
    "            if seq:\n",
    "                for char in BASES:\n",
    "                    if row['sequence'][index]==char:\n",
    "                        arr[index,ctr]+=1\n",
    "                    ctr+=1\n",
    "\n",
    "            if struct:\n",
    "                loop_assignments = ''.join(ensemble[:,index])\n",
    "                for char in STRUCTS:\n",
    "                    prob = loop_assignments.count(char) / len(loop_assignments)\n",
    "                    arr[index,ctr]+=prob\n",
    "                    ctr+=1\n",
    "                    \n",
    "        # add zero padding to the side\n",
    "        padded_arr = np.vstack([np.zeros([window_size,len(feature_kernel)]),arr[pad:], np.zeros([window_size,len(feature_kernel)])])\n",
    "\n",
    "        for index in range(pad,MAX_LEN):\n",
    "            new_index = index+window_size-pad\n",
    "            tmp = padded_arr[new_index-window_size:new_index+window_size+1]\n",
    "            inpts.append(tmp.flatten())\n",
    "            labels.append('%s_%d' % (row['id'], index))\n",
    "            \n",
    "    return np.array(inpts), feature_names, labels\n",
    "\n",
    "def encode_output(df, data_type='reactivity', pad=10):\n",
    "    '''Creat input/output for regression model for predicting structure probing data.\n",
    "    Inputs:\n",
    "    \n",
    "    dataframe (in EternaBench RDAT format)\n",
    "    data_type: column name for degradation\n",
    "    window_size: size of window (in one direction). so window_size=1 is a total window size of 3\n",
    "    pad: number of nucleotides at start to not include\n",
    "    \n",
    "    Outputs:\n",
    "    output array (n_samples): array of reactivity values\n",
    "    \n",
    "    '''\n",
    "    #MAX_LEN = 68\n",
    "    \n",
    "    outpts = []\n",
    "    labels = []\n",
    "    # output identity should be in form id_00073f8be_0\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        MAX_LEN = len(row['sequence'])-39\n",
    "        \n",
    "        for index in range(pad,MAX_LEN):\n",
    "            outpts.append(row[data_type][index])\n",
    "            labels.append('%s_%d' % (row['id'], index))\n",
    "            \n",
    "            \n",
    "    return outpts, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_input_construct(df, window_size=1, pad=10, seq=True, struct=True, ensemble_size=0):\n",
    "    '''Creat input/output for regression model for predicting structure probing data.\n",
    "    Inputs:\n",
    "    \n",
    "    dataframe (in EternaBench RDAT format)\n",
    "    window_size: size of window (in one direction). so window_size=1 is a total window size of 3\n",
    "    pad: number of nucleotides at start to not include\n",
    "    seq (bool): include sequence encoding\n",
    "    struct (bool): include bpRNA structure encoding\n",
    "    \n",
    "    Outputs:\n",
    "    Input array (n_samples x n_features): array of windowed input features\n",
    "    feature_names (list): feature names\n",
    "    \n",
    "    '''\n",
    "    #MAX_LEN = 68\n",
    "    BASES = ['A','U','G','C']\n",
    "    STRUCTS = ['H','E','I','M','B','S']\n",
    "    \n",
    "    inpts = []\n",
    "    labels = []\n",
    "\n",
    "    feature_kernel=[]\n",
    "    if seq:\n",
    "        feature_kernel.extend(BASES)\n",
    "    if struct:\n",
    "        feature_kernel.extend(STRUCTS)\n",
    "\n",
    "    feature_names = ['%s_%d' % (k, val) for val in range(-1*window_size, window_size+1) for k in feature_kernel]\n",
    "    \n",
    "    for i, row in tqdm(df.iterrows(), desc='Encoding inputs', total=len(df)):\n",
    "        MAX_LEN = len(row['sequence'])-39 #68 for RYOS-I\n",
    "        \n",
    "        arr = np.zeros([MAX_LEN,len(feature_kernel)])\n",
    "        \n",
    "        if ensemble_size > 0: # stochastically sample ensemble\n",
    "            ensemble = get_ensemble(row['sequence'], n=ensemble_size)\n",
    "        else: # use MEA structure\n",
    "            ensemble = np.array([list(row['predicted_loop_type'])])\n",
    "\n",
    "        for index in range(pad,MAX_LEN):\n",
    "            ctr=0\n",
    "\n",
    "            #encode sequence\n",
    "            if seq:\n",
    "                for char in BASES:\n",
    "                    if row['sequence'][index]==char:\n",
    "                        arr[index,ctr]+=1\n",
    "                    ctr+=1\n",
    "\n",
    "            if struct:\n",
    "                loop_assignments = ''.join(ensemble[:,index])\n",
    "                for char in STRUCTS:\n",
    "                    prob = loop_assignments.count(char) / len(loop_assignments)\n",
    "                    arr[index,ctr]+=prob\n",
    "                    ctr+=1\n",
    "                    \n",
    "        # add zero padding to the side\n",
    "        padded_arr = np.vstack([np.zeros([window_size,len(feature_kernel)]),arr[pad:], np.zeros([window_size,len(feature_kernel)])])\n",
    "\n",
    "        tmp_construct_holder = np.zeros((window_size*2+1)*len(feature_kernel))\n",
    "        \n",
    "        for index in range(pad,MAX_LEN):\n",
    "            new_index = index+window_size-pad\n",
    "            tmp = padded_arr[new_index-window_size:new_index+window_size+1]\n",
    "                        \n",
    "            tmp_construct_holder = tmp_construct_holder+tmp.flatten()\n",
    "            labels.append('%s_%d' % (row['id'], index))\n",
    "            \n",
    "        inpts.append(tmp_construct_holder)\n",
    "\n",
    "    return np.array(inpts), feature_names, labels\n",
    "\n",
    "def encode_output_construct(df, data_type='reactivity', pad=10):\n",
    "    '''Creat input/output for regression model for predicting structure probing data.\n",
    "    Inputs:\n",
    "    \n",
    "    dataframe (in EternaBench RDAT format)\n",
    "    data_type: column name for degradation\n",
    "    window_size: size of window (in one direction). so window_size=1 is a total window size of 3\n",
    "    pad: number of nucleotides at start to not include\n",
    "    \n",
    "    Outputs:\n",
    "    output array (n_samples): array of reactivity values\n",
    "    \n",
    "    '''\n",
    "    #MAX_LEN = 68\n",
    "    \n",
    "    outpts = []\n",
    "    labels = []\n",
    "    # output identity should be in form id_00073f8be_0\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        MAX_LEN = len(row['sequence'])-39\n",
    "        outpts.append(np.sum([row[data_type][index] for index in range(pad,MAX_LEN)]))\n",
    "            \n",
    "    return outpts, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_train = pd.read_json('train.json',lines=True)\n",
    "kaggle_train = kaggle_train.loc[kaggle_train['SN_filter']==1]\n",
    "\n",
    "kaggle_test = pd.read_json('test.json',lines=True)\n",
    "#kaggle_test = pd.read_csv('test_labels.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Encode data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Max. expected accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1827cfc7270d424599bf6db42fc292ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Encoding inputs', max=1589, style=ProgressStyle(description_w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c04813e5b1974813938e487389853daa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Encoding inputs', max=3634, style=ProgressStyle(description_w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "mea_inputs_train, mea_feature_names, _ = encode_input(kaggle_train, window_size=2)\n",
    "mea_inputs_test, _, mea_test_labels = encode_input(kaggle_test, window_size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "252e12c4ab3347e1927e3a4dfea7f6e1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Encoding inputs', max=1589, style=ProgressStyle(description_w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "mea_inputs_train_construct, mea_feature_names, _ = encode_input_construct(kaggle_train, window_size=12)\n",
    "#mea_inputs_test, _, mea_test_labels = encode_input(kaggle_test, window_size=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Ensemble-averaged encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d2b083126434bfe843eb61fe1de3fbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Encoding inputs', max=1589, style=ProgressStyle(description_w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d24691c20d2047b08e1fd0a0fc54b9b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Encoding inputs', max=3634, style=ProgressStyle(description_w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ensemble_size = 100\n",
    "ens_inputs_train, ens_feature_names, _ = encode_input(kaggle_train, window_size=12, ensemble_size=ensemble_size)\n",
    "ens_inputs_test, _, ens_test_labels = encode_input(kaggle_test, window_size=12, ensemble_size=ensemble_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize two encodings for an example nucleotide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'window position')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(10,4))\n",
    "subplot(1,2,1)\n",
    "title('MFE encoding')\n",
    "imshow(np.array(mea_inputs_train[33].reshape(25,10)).T,cmap='gist_heat_r')\n",
    "yticks(range(10), ['A','U','G','C','H','E','I','M','B','S'])\n",
    "xlabel('window position')\n",
    "subplot(1,2,2)\n",
    "title('ensemble encoding')\n",
    "imshow(np.array(ens_inputs_train[33].reshape(25,10)).T,cmap='gist_heat_r')\n",
    "yticks(range(10), ['A','U','G','C','H','E','I','M','B','S'])\n",
    "xlabel('window position')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setup kaggle submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission = pd.read_csv('sample_submission.csv.zip')\n",
    "mask = sample_submission['id_seqpos'].isin(mea_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "ens_sample_submission = pd.read_csv('sample_submission.csv.zip')\n",
    "mask = ens_sample_submission['id_seqpos'].isin(ens_test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### MEA models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting deg_Mg_pH10 ...\n"
     ]
    }
   ],
   "source": [
    "mea_models = {}\n",
    "\n",
    "for output_type in ['deg_Mg_pH10']: #['reactivity', 'deg_Mg_pH10', 'deg_pH10', 'deg_Mg_50C','deg_50C']:\n",
    "    mea_outputs_train, mea_outputs_labels = encode_output(kaggle_train, data_type=output_type)\n",
    "    #mea_outputs_train, mea_outputs_labels = encode_output_construct(kaggle_train, data_type=output_type)\n",
    "\n",
    "    # Clip negative values to 0\n",
    "    #mea_outputs_train = np.clip(mea_outputs_train, -1, 100)\n",
    "\n",
    "    reg = Ridge(alpha=0.15, fit_intercept=False)\n",
    "    print('Fitting %s ...' % output_type)\n",
    "    #reg.fit(mea_inputs_train_construct, mea_outputs_train)\n",
    "    reg.fit(mea_inputs_train, mea_outputs_train)\n",
    "    \n",
    "    mea_models[output_type] = reg\n",
    "    \n",
    "    # test inputs, add to sample submission df\n",
    "    test_prediction = reg.predict(mea_inputs_test)\n",
    "    #sample_submission.loc[mask, output_type] = test_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'-0.119975,-0.109280,-0.125776,-0.129901,0.137878,0.048720,0.129931,0.079754,0.123292,0.098688,-0.005116,0.035615,-0.031198,-0.061845,0.049366,-0.030215,0.016736,0.018498,0.016610,-0.034317,0.859800,0.932746,0.916964,0.827843,-0.068651,-0.063083,-0.145654,0.010412,0.059719,-0.278208,-0.068793,0.232126,-0.258597,-0.231803,-0.074957,-0.083229,-0.013723,0.007611,0.069522,-0.082664,0.046571,0.009784,-0.087292,-0.024710,-0.015972,-0.188900,-0.002574,-0.002828,0.009722,-0.080645'"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "','.join([\"%.6f\" % x for x in reg.coef_])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = DegScore('AAA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sequence': 'AAA',\n",
       " 'structure': '...',\n",
       " 'bprna_string': 'EEE',\n",
       " 'coeffs_': [-0.02,\n",
       "  -0.027,\n",
       "  -0.026,\n",
       "  -0.017,\n",
       "  0.005,\n",
       "  0.0,\n",
       "  0.005,\n",
       "  -0.006,\n",
       "  -0.011,\n",
       "  0.006,\n",
       "  0.031,\n",
       "  0.021,\n",
       "  0.036,\n",
       "  0.034,\n",
       "  0.026,\n",
       "  -0.024,\n",
       "  -0.005,\n",
       "  0.028,\n",
       "  -0.022,\n",
       "  -0.015,\n",
       "  -0.043,\n",
       "  -0.043,\n",
       "  -0.029,\n",
       "  -0.026,\n",
       "  0.016,\n",
       "  -0.077,\n",
       "  -0.001,\n",
       "  -0.016,\n",
       "  0.031,\n",
       "  -0.001,\n",
       "  0.064,\n",
       "  0.065,\n",
       "  0.064,\n",
       "  0.069,\n",
       "  0.029,\n",
       "  0.044,\n",
       "  -0.003,\n",
       "  0.012,\n",
       "  -0.006,\n",
       "  -0.004,\n",
       "  -0.072,\n",
       "  -0.066,\n",
       "  -0.061,\n",
       "  -0.065,\n",
       "  0.014,\n",
       "  0.037,\n",
       "  0.051,\n",
       "  0.017,\n",
       "  0.054,\n",
       "  0.037,\n",
       "  -0.065,\n",
       "  -0.068,\n",
       "  -0.058,\n",
       "  -0.041,\n",
       "  -0.014,\n",
       "  0.075,\n",
       "  -0.007,\n",
       "  0.005,\n",
       "  -0.01,\n",
       "  -0.006,\n",
       "  0.009,\n",
       "  0.014,\n",
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       " 'degscore_by_position': array([0.699, 0.595, 1.158]),\n",
       " 'degscore': 2.452}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x120d96898>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(20,5))\n",
    "imshow(np.vstack([x[:68] for x in kaggle_train['deg_Mg_pH10']]).T, aspect=10,cmap='RdYlBu_r')\n",
    "colorbar(fraction=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Ensemble-averaged models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting reactivity ...\n",
      "Fitting deg_Mg_pH10 ...\n",
      "Fitting deg_pH10 ...\n",
      "Fitting deg_Mg_50C ...\n",
      "Fitting deg_50C ...\n"
     ]
    }
   ],
   "source": [
    "ens_models = {}\n",
    "\n",
    "for output_type in ['reactivity', 'deg_Mg_pH10', 'deg_pH10', 'deg_Mg_50C','deg_50C']:\n",
    "    ens_outputs_train, ens_outputs_labels = encode_output(kaggle_train, data_type=output_type)\n",
    "    \n",
    "    # Clip negative values to 0\n",
    "    ens_outputs_train = np.clip(ens_outputs_train, 0, 100)\n",
    "\n",
    "    reg = Ridge(alpha=0.15)\n",
    "    print('Fitting %s ...' % output_type)\n",
    "    reg.fit(ens_inputs_train, ens_outputs_train)\n",
    "    \n",
    "    ens_models[output_type] = reg\n",
    "    \n",
    "    # test inputs, add to sample submission df\n",
    "    test_prediction = reg.predict(ens_inputs_test)\n",
    "    ens_sample_submission.loc[mask, output_type] = test_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save to csv\n",
    "sample_submission.to_csv('test_mea_code_jan28.csv', index=False)\n",
    "ens_sample_submission.to_csv('test_ens_code_jan28.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the existing class that contains \"DegScore-2.1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_mcrmse(models, test_df):\n",
    "    scored_data_types = ['reactivity', 'deg_Mg_pH10', 'deg_Mg_50C']\n",
    "\n",
    "    seq_scores = []\n",
    "    for i, row in test_df.iterrows():\n",
    "        scores = []\n",
    "        for data_type in scored_data_types:\n",
    "            model = models[data_type]\n",
    "            \n",
    "            coeffs = model.coef_\n",
    "            inter = model.intercept_\n",
    "            \n",
    "            mdl = DegScore.DegScore(row['sequence'], structure=row['structure'], coeffs=coeffs, intercept=inter)\n",
    "            \n",
    "            seq_scored = row['seq_scored']\n",
    "            pred = mdl.degscore_by_position[:seq_scored]\n",
    "            target = np.array(json.loads(row[data_type])[:seq_scored])\n",
    "            \n",
    "            scores.extend(np.square(pred - target))\n",
    "        \n",
    "        seq_scores.append(scores)\n",
    "        \n",
    "    return np.mean(seq_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24691545925204095"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_mcrmse(mea_models, kaggle_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23856658661289426"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_mcrmse(ens_models, kaggle_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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